

import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase			:				str   =     logging.get_logger(__name__)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[Any]      		=					OrderedDict()
							for key, value in state_dict.items():
														if key.startswith('''module.encoder'''		):
																					A      :					Any      		=					key.replace('''module.encoder'''     ,				'''glpn.encoder'''		)
														if key.startswith('''module.decoder'''		):
																					A      :					int      		=					key.replace('''module.decoder'''     ,				'''decoder.stages'''		)
														if "patch_embed" in key:
																					# replace for example patch_embed1 by patch_embeddings.0
																					A      :					Any      		=					key[key.find('''patch_embed'''		) + len('''patch_embed'''		)]
																					A      :					int      		=					key.replace(F'patch_embed{idx}'     ,				F'patch_embeddings.{int(lowerCamelCase_		)-1}'		)
														if "norm" in key:
																					A      :					str      		=					key.replace('''norm'''     ,				'''layer_norm'''		)
														if "glpn.encoder.layer_norm" in key:
																					# replace for example layer_norm1 by layer_norm.0
																					A      :					str      		=					key[key.find('''glpn.encoder.layer_norm'''		) + len('''glpn.encoder.layer_norm'''		)]
																					A      :					List[Any]      		=					key.replace(F'layer_norm{idx}'     ,				F'layer_norm.{int(lowerCamelCase_		)-1}'		)
														if "layer_norm1" in key:
																					A      :					Union[str, Any]      		=					key.replace('''layer_norm1'''     ,				'''layer_norm_1'''		)
														if "layer_norm2" in key:
																					A      :					Optional[Any]      		=					key.replace('''layer_norm2'''     ,				'''layer_norm_2'''		)
														if "block" in key:
																					# replace for example block1 by block.0
																					A      :					int      		=					key[key.find('''block'''		) + len('''block'''		)]
																					A      :					Tuple      		=					key.replace(F'block{idx}'     ,				F'block.{int(lowerCamelCase_		)-1}'		)
														if "attn.q" in key:
																					A      :					List[Any]      		=					key.replace('''attn.q'''     ,				'''attention.self.query'''		)
														if "attn.proj" in key:
																					A      :					List[Any]      		=					key.replace('''attn.proj'''     ,				'''attention.output.dense'''		)
														if "attn" in key:
																					A      :					str      		=					key.replace('''attn'''     ,				'''attention.self'''		)
														if "fc1" in key:
																					A      :					int      		=					key.replace('''fc1'''     ,				'''dense1'''		)
														if "fc2" in key:
																					A      :					List[str]      		=					key.replace('''fc2'''     ,				'''dense2'''		)
														if "linear_pred" in key:
																					A      :					List[str]      		=					key.replace('''linear_pred'''     ,				'''classifier'''		)
														if "linear_fuse" in key:
																					A      :					Optional[Any]      		=					key.replace('''linear_fuse.conv'''     ,				'''linear_fuse'''		)
																					A      :					Any      		=					key.replace('''linear_fuse.bn'''     ,				'''batch_norm'''		)
														if "linear_c" in key:
																					# replace for example linear_c4 by linear_c.3
																					A      :					List[Any]      		=					key[key.find('''linear_c'''		) + len('''linear_c'''		)]
																					A      :					List[Any]      		=					key.replace(F'linear_c{idx}'     ,				F'linear_c.{int(lowerCamelCase_		)-1}'		)
														if "bot_conv" in key:
																					A      :					Union[str, Any]      		=					key.replace('''bot_conv'''     ,				'''0.convolution'''		)
														if "skip_conv1" in key:
																					A      :					Optional[int]      		=					key.replace('''skip_conv1'''     ,				'''1.convolution'''		)
														if "skip_conv2" in key:
																					A      :					Union[str, Any]      		=					key.replace('''skip_conv2'''     ,				'''2.convolution'''		)
														if "fusion1" in key:
																					A      :					Dict      		=					key.replace('''fusion1'''     ,				'''1.fusion'''		)
														if "fusion2" in key:
																					A      :					List[str]      		=					key.replace('''fusion2'''     ,				'''2.fusion'''		)
														if "fusion3" in key:
																					A      :					Tuple      		=					key.replace('''fusion3'''     ,				'''3.fusion'''		)
														if "fusion" in key and "conv" in key:
																					A      :					List[str]      		=					key.replace('''conv'''     ,				'''convolutional_layer'''		)
														if key.startswith('''module.last_layer_depth'''		):
																					A      :					List[str]      		=					key.replace('''module.last_layer_depth'''     ,				'''head.head'''		)
														A      :					List[Any]      		=					value

							return new_state_dict






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							# for each of the encoder blocks:
							for i in range(config.num_encoder_blocks		):
														for j in range(config.depths[i]		):
																					# read in weights + bias of keys and values (which is a single matrix in the original implementation)
																					A      :					Any      		=					state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'		)
																					A      :					str      		=					state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias'		)
																					# next, add keys and values (in that order) to the state dict
																					A      :					Union[str, Any]      		=					kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					A      :					int      		=					kv_bias[: config.hidden_sizes[i]]
																					A      :					str      		=					kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					A      :					Optional[int]      		=					kv_bias[config.hidden_sizes[i] :]






def        snake_case__		(    ):
							A      :					Union[str, Any]      		=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							A      :					str      		=					Image.open(requests.get(lowerCamelCase_     ,				stream=lowerCamelCase_		).raw		)

							return image






@torch.no_grad()
def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_=False     ,				lowerCamelCase_=None		):
							A      :					Optional[int]      		=					GLPNConfig(hidden_sizes=[64, 128, 320, 512]     ,				decoder_hidden_size=64     ,				depths=[3, 8, 27, 3]		)

							# load image processor (only resize + rescale)
							A      :					int      		=					GLPNImageProcessor()

							# prepare image
							A      :					Tuple      		=					prepare_img()
							A      :					str      		=					image_processor(images=lowerCamelCase_     ,				return_tensors='''pt'''		).pixel_values

							logger.info('''Converting model...'''		)

							# load original state dict
							A      :					Optional[int]      		=					torch.load(lowerCamelCase_     ,				map_location=torch.device('''cpu'''		)		)

							# rename keys
							A      :					Tuple      		=					rename_keys(lowerCamelCase_		)

							# key and value matrices need special treatment
							read_in_k_v(lowerCamelCase_     ,				lowerCamelCase_		)

							# create HuggingFace model and load state dict
							A      :					List[str]      		=					GLPNForDepthEstimation(lowerCamelCase_		)
							model.load_state_dict(lowerCamelCase_		)
							model.eval()

							# forward pass
							A      :					Optional[Any]      		=					model(lowerCamelCase_		)
							A      :					Union[str, Any]      		=					outputs.predicted_depth

							# verify output
							if model_name is not None:
														if "nyu" in model_name:
																					A      :					Optional[int]      		=					torch.tensor(
																					    [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]		)
														elif "kitti" in model_name:
																					A      :					Dict      		=					torch.tensor(
																					    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]		)
														else:
																					raise ValueError(F'Unknown model name: {model_name}'		)

														A      :					List[Any]      		=					torch.Size([1, 480, 640]		)

														assert predicted_depth.shape == expected_shape
														assert torch.allclose(predicted_depth[0, :3, :3]     ,				lowerCamelCase_     ,				atol=1E-4		)
														print('''Looks ok!'''		)

							# finally, push to hub if required
							if push_to_hub:
														logger.info('''Pushing model and image processor to the hub...'''		)
														model.push_to_hub(
														    repo_path_or_name=Path(lowerCamelCase_     ,				lowerCamelCase_		)     ,				organization='''nielsr'''     ,				commit_message='''Add model'''     ,				use_temp_dir=lowerCamelCase_     ,				)
														image_processor.push_to_hub(
														    repo_path_or_name=Path(lowerCamelCase_     ,				lowerCamelCase_		)     ,				organization='''nielsr'''     ,				commit_message='''Add image processor'''     ,				use_temp_dir=lowerCamelCase_     ,				)


if __name__ == "__main__":
		lowercase			:				str   =     argparse.ArgumentParser()

		parser.add_argument(
		    "--checkpoint_path",
		    default=None,
		    type=str,
		    help="Path to the original PyTorch checkpoint (.pth file).",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
		)
		parser.add_argument(
		    "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
		)
		parser.add_argument(
		    "--model_name",
		    default="glpn-kitti",
		    type=str,
		    help="Name of the model in case you're pushing to the hub.",
		)
		lowercase			:				Union[str, Any]   =     parser.parse_args()
		convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









def        snake_case__		(    lowerCamelCase_ = 1000		):
							return sum(e for e in range(3     ,				lowerCamelCase_		) if e % 3 == 0 or e % 5 == 0		)


if __name__ == "__main__":
		print(F"{solution() = }")






